Challenges in nodal peripheral T-cell lymphomas: from biological advances to clinical applicability

Prediction of Lymphoma Aggressiveness Using Machine Learning Algorithms

INTRODUCTION

Lymph nodes are essential to diagnose lymphoid neoplasms, metastases, and infections. Some lymphomas, particularly aggressive non-Hodgkin lymphomas (NHL), need urgent diagnosis. Combining lymph node cytology (LNC) and flow cytometry (FC) with other rapidly available parameters through multivariable predictive models could offer valuable diagnostic information while waiting for anatomopathological results.

MATERIALS AND METHODS

Results of 196 lymph node specimens were retrospectively analyzed for parameters like age, sex, LNC, FC, positron emission tomography scan, lymphocytosis, leukocytosis, lactate dehydrogenase (LDH) levels, and hemoglobin. We constructed five multivariable models predicting the aggressive nature of lymphoma as defined by the anatomopathological diagnostic. The first three were logistic regression models based on two (model 1), four (model 2), and up to 16 independent variables (model 3). The last two models were based on ensemble learning algorithms, bagging (model 4) and boosting (model 5), respectively. The performance of these five models was compared after 10-fold cross-validation, evaluating metrics such as sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).

RESULTS

Compared to individual variables associated with the aggressive nature of the lymphoma (AUCs from 0.69 to 0.87), the multivariable models achieved better AUCs, ranging from 0.88 to 0.94. The best model (model 5) achieved a sensitivity and a specificity of 77% and 94%, respectively.

CONCLUSION

LNC, FC, and other rapidly available parameters are associated with the aggressive nature of the lymphomas. It is possible to combine them in multivariable models to obtain a valuable diagnostic information and to initiate a prompt treatment.

Novel serous effusion-related risk models and biomarkers for predicting prognosis in T-cell lymphoma patients

T-cell lymphomas (TCLs) are a cluster of lymphoproliferative diseases with high heterogeneity, which lack accurate prognostic models and standard treatment regimen at present. Serous effusion (SE) is a relatively common manifestation and poses more challenges for risk stratification in TCLs. In this study, entire of 518 newly diagnosed TCLs patients were included. SE was found to be tightly correlated to clinical characteristics and prognosis in TCL patients, and SE volume (SEV) > 1000 ml was identified as a potential prognostic factor. Novel AEBS risk model, including age > 60, ECOG PS > 1, β2-microglobulin (BMG) > 3.0 mg/L and SEV > 1000 ml, which exerted superior efficacy for risk stratification compared to the current risk systems in TCL patients with SE. Besides, multiple RNA-seq datasets were used for the identification and function analysis of SE-related genes (SERGs). TCL patients in different SERGs-associated subgroups exhibited discrepancy in the infiltration of immunocytes and the expression of immune checkpoints. SERGs signature, including HIF1A, FERMT2, NFATC1 and COL1A1, was established and demonstrated to have distinguishing capacity for predicting prognosis in TCL patients. Moreover, immunohistochemistry revealed that SE-related molecule HIF1A was reductively expressed and related to inferior prognosis in TCL patients, especially in SE group. Pan-cancer analysis found HIF1A expression was decreased in several tumors, and chemosensitivity analysis revealed that HIF1A was associated with sensitivity of several anti-tumor drugs, such as Sorafenib, Navitoclax, and Venetoclax. Our findings provide evidence for identifying high-risk population and facilitating individualized treatment in TCL patients with SE.

Genome-Wide Methylation Profiling of Peripheral T-Cell Lymphomas Identifies TRIP13 as a Critical Driver of Tumor Proliferation and Survival

Cytosine methylation contributes to the regulation of gene expression and normal hematopoiesis in mammals. It is catalyzed by the family of DNA methyltransferases that include DNMT1, DNMT3A, and DNMT3B. Peripheral T-cell lymphomas (PTCLs) represent aggressive mature T-cell malignancies exhibiting a broad spectrum of clinical features with poor prognosis and inadequately understood molecular pathobiology. To better understand the molecular landscape and identify candidate genes involved in disease maintenance, we profiled DNA methylation and gene expression of PTCLs. We found that the methylation patterns in PTCLs are deregulated and heterogeneous but share 767 hypo- and 567 hypermethylated differentially methylated regions (DMRs) along with 231 genes up- and 91 genes downregulated in all samples, suggesting a potential association with tumor development. We further identified 39 hypomethylated promoters associated with increased gene expression in the majority of PTCLs. This putative oncogenic signature included the TRIP13 (thyroid hormone receptor interactor 13) gene whose genetic and pharmacologic inactivation inhibited the proliferation of T-cell lines by inducing G2-M arrest and apoptosis. Our data thus show that human PTCLs have a significant number of recurrent methylation alterations that may affect the expression of genes critical for proliferation whose targeting might be beneficial in anti-lymphoma treatments.

Genome-wide methylation profiling of Peripheral T-cell lymphomas identifies TRIP13 as a critical driver of tumor proliferation and survival

Cytosine methylation of genomic DNA contributes to the regulation of gene expression and is involved in normal development including hematopoiesis in mammals. It is catalyzed by the family of DNA methyltransferases (DNMTs) that include DNMT1, DNMT3A, and DNMT3B. Peripheral T-cell lymphomas (PTCLs) represent a diverse group of aggressive mature T-cell malignancies accounting for approximately 10-15% of non-Hodgkin lymphoma cases in the US. PTCLs exhibit a broad spectrum of clinical, histological, and immunophenotypic features with poor prognosis and inadequately understood molecular pathobiology. To better understand the molecular landscape and identify candidate genes involved in disease maintenance, we used high-resolution Whole Genome Bisulfite Sequencing (WGBS) and RNA-seq to profile DNA methylation and gene expression of PTCLs and normal T-cells. We found that the methylation patterns in PTCLs are deregulated and heterogeneous but share 767 hypo- and 567 hypermethylated differentially methylated regions (DMRs) along with 231 genes up- and 91 genes downregulated in all samples suggesting a potential association with tumor development. We further identified 39 hypomethylated promoters associated with increased gene expression in the majority of PTCLs. This putative oncogenic signature included the TRIP13 (thyroid hormone receptor interactor 13) gene whose both genetic and pharmacologic inactivation, inhibited cellular growth of PTCL cell lines by inducing G2-M arrest accompanied by apoptosis suggesting that such an approach might be beneficial in human lymphoma treatment. Altogether we show that human PTCLs are characterized by a large number of recurrent methylation alterations, and demonstrated that TRIP13 is critical for PTCL maintenance in vitro.

The Novel Link between Gene Expression Profiles of Adult T-Cell Leukemia/Lymphoma Patients' Peripheral Blood Lymphocytes and Ferroptosis Susceptibility

Ferroptosis, a regulated cell death dependent on iron, has garnered attention as a potential broad-spectrum anticancer approach in leukemia research. However, there has been limited ferroptosis research on ATL, an aggressive T-cell malignancy caused by HTLV-1 infection. Our study employs bioinformatic analysis, utilizing dataset GSE33615, to identify 46 ferroptosis-related DEGs and 26 autophagy-related DEGs in ATL cells. These DEGs are associated with various cellular responses, chemical stress, and iron-related pathways. Autophagy-related DEGs are linked to autophagy, apoptosis, NOD-like receptor signaling, TNF signaling, and the insulin resistance pathway. PPI network analysis revealed 10 hub genes and related biomolecules. Moreover, we predicted crucial miRNAs, transcription factors, and potential pharmacological compounds. We also screened the top 20 medications based on upregulated DEGs. In summary, our study establishes an innovative link between ATL treatment and ferroptosis, offering promising avenues for novel therapeutic strategies in ATL.

Extranodal lymphoma: pathogenesis, diagnosis and treatment

Approximately 30% of lymphomas occur outside the lymph nodes, spleen, or bone marrow, and the incidence of extranodal lymphoma has been rising in the past decade. While traditional chemotherapy and radiation therapy can improve survival outcomes for certain patients, the prognosis for extranodal lymphoma patients remains unsatisfactory. Extranodal lymphomas in different anatomical sites often have distinct cellular origins, pathogenic mechanisms, and clinical manifestations, significantly influencing their diagnosis and treatment. Therefore, it is necessary to provide a comprehensive summary of the pathogenesis, diagnosis, and treatment progress of extranodal lymphoma overall and specifically for different anatomical sites. This review summarizes the current progress in the common key signaling pathways in the development of extranodal lymphomas and intervention therapy. Furthermore, it provides insights into the pathogenesis, diagnosis, and treatment strategies of common extranodal lymphomas, including gastric mucosa-associated lymphoid tissue (MALT) lymphoma, mycosis fungoides (MF), natural killer/T-cell lymphoma (nasal type, NKTCL-NT), and primary central nervous system lymphoma (PCNSL). Additionally, as PCNSL is one of the extranodal lymphomas with the worst prognosis, this review specifically summarizes prognostic indicators and discusses the challenges and opportunities related to its clinical applications. The aim of this review is to assist clinical physicians and researchers in understanding the current status of extranodal lymphomas, enabling them to make informed clinical decisions that contribute to improving patient prognosis.